Munro Madison H, Gore Ross J, Lynch Christopher J, Hastings Yvette D, Reinhold Ann Marie
Gianforte School of Computing, Montana State University, Bozeman, Montana, USA.
Virginia Modeling, Analysis and Simulation Center, Old Dominion University, Suffolk, Virginia, USA.
Risk Anal. 2025 Jul;45(7):1683-1697. doi: 10.1111/risa.17690. Epub 2024 Dec 15.
Recent developments in risk and crisis communication (RCC) research combine social science theory and data science tools to construct effective risk messages efficiently. However, current systematic literature reviews (SLRs) on RCC primarily focus on computationally assessing message efficacy as opposed to message efficiency. We conduct an SLR to highlight any current computational methods that improve message construction efficacy and efficiency. We found that most RCC research focuses on using theoretical frameworks and computational methods to analyze or classify message elements that improve efficacy. For improving message efficiency, computational and manual methods are only used in message classification. Specifying the computational methods used in message construction is sparse. We recommend that future RCC research apply computational methods toward improving efficacy and efficiency in message construction. By improving message construction efficacy and efficiency, RCC messaging would quickly warn and better inform affected communities impacted by current hazards. Such messaging has the potential to save as many lives as possible.
风险与危机沟通（RCC）研究的最新进展将社会科学理论与数据科学工具相结合，以高效构建有效的风险信息。然而，当前关于RCC的系统文献综述（SLR）主要侧重于通过计算评估信息效果，而非信息效率。我们进行了一项SLR，以突出当前任何可提高信息构建效果和效率的计算方法。我们发现，大多数RCC研究专注于使用理论框架和计算方法来分析或分类可提高效果的信息元素。为了提高信息效率，计算和人工方法仅用于信息分类。在信息构建中使用的计算方法的具体说明很少。我们建议未来的RCC研究应用计算方法来提高信息构建的效果和效率。通过提高信息构建的效果和效率，RCC信息将能够迅速发出警告，并更好地向受当前灾害影响的社区提供信息。这样的信息有潜力挽救尽可能多的生命。
